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Assessing systemic risk in financial markets using dynamic topic networks

Scientific reports, 2022-02, Vol.12 (1), p.2668-2668, Article 2668 [Peer Reviewed Journal]

2022. The Author(s). ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2022 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-022-06399-x ;PMID: 35177679

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  • Title:
    Assessing systemic risk in financial markets using dynamic topic networks
  • Author: So, Mike K P ; Mak, Anson S W ; Chu, Amanda M Y
  • Subjects: Coronaviruses ; COVID-19 ; Disease transmission ; Dow Jones averages ; Operations management ; Pandemics ; Securities markets ; Sovereign debt ; Stock exchanges ; Volatility
  • Is Part Of: Scientific reports, 2022-02, Vol.12 (1), p.2668-2668, Article 2668
  • Description: Systemic risk in financial markets refers to the breakdown of a financial system due to global events, catastrophes, or extreme incidents, leading to huge financial instability and losses. This study proposes a dynamic topic network (DTN) approach that combines topic modelling and network analysis to assess systemic risk in financial markets. We make use of Latent Dirichlet Allocation (LDA) to semantically analyse news articles, and the extracted topics then serve as input to construct topic similarity networks over time. Our results indicate how connected the topics are so that we can correlate any abnormal behaviours with volatility in the financial markets. With the 2015-2016 stock market selloff and COVID-19 as use cases, our results also suggest that the proposed DTN approach can provide an indication of (a) abnormal movement in the Dow Jones Industrial Average and (b) when the market would gradually begin to recover from such an event. From a practical risk management point of view, this analysis can be carried out on a daily basis when new data come in so that we can make use of the calculated metrics to predict real-time systemic risk in financial markets.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-022-06399-x
    PMID: 35177679
  • Source: PubMed Central
    Coronavirus Research Database
    ProQuest Central
    DOAJ Directory of Open Access Journals
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